--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/formatter.py	Tue Mar 22 16:08:45 2022 +0000
@@ -0,0 +1,124 @@
+import click
+from pandas import DataFrame, read_csv
+
+
+def create_long_table(data: DataFrame, value_id: str) -> DataFrame:
+    """Convert the table from compact into long format.
+    See DataFrame.melt(...).
+
+    Args:
+        data (DataFrame): The data table to convert.
+        value_id (str): The name to assign to the added column through conversion to long format.
+
+    Returns:
+        DataFrame: Table in long format.
+    """
+    return data.transpose().melt(ignore_index=False, var_name='compound', value_name=value_id)
+
+
+def join_df(x: DataFrame, y: DataFrame, on=[], how="inner") -> DataFrame:
+    """Shortcut functions to join to dataframes on columns and index
+
+    Args:
+        x (DataFrame): Table X
+        y (DataFrame): Table Y
+        on (list, optional): Columns on which to join. Defaults to [].
+        how (str, optional): Join method, see DataFrame.join(...). Defaults to "inner".
+
+    Returns:
+        DataFrame: Joined dataframe.
+    """
+    df_x = x.set_index([x.index] + on)
+    df_y = y.set_index([y.index] + on)
+    combined = df_x.join(df_y, how=how)
+    return combined
+
+
+def get_top_k_matches(data: DataFrame, k: int) -> DataFrame:
+    """Function to get top k matches from dataframe with scores.
+
+    Args:
+        data (DataFrame): A table with score column.
+        k (int): Number of top scores to retrieve.
+
+    Returns:
+        DataFrame: Table containing only the top k best matches for each compound.
+    """
+    return data.groupby(level=0, group_keys=False).apply(DataFrame.nlargest, n=k, columns=['score'])
+
+
+def filter_thresholds(data: DataFrame, t_score: float, t_matches: float) -> DataFrame:
+    """Filter a dataframe with scores and matches to only contain values above specified thresholds.
+
+    Args:
+        data (DataFrame): Table to filter.
+        t_score (float): Score threshold.
+        t_matches (float): Matches threshold.
+
+    Returns:
+        DataFrame: Filtered dataframe.
+    """
+    filtered = data[data['score'] > t_score]
+    filtered = filtered[filtered['matches'] > t_matches]
+    return filtered
+
+
+def load_data(scores_filename: str, matches_filename: str) -> DataFrame:
+    """Load data from filenames and join on compound id.
+
+    Args:
+        scores_filename (str): Path to scores table.
+        matches_filename (str): Path to matches table.
+
+    Returns:
+        DataFrame: Joined dataframe on compounds containing scores an matches in long format.
+    """
+    matches = read_csv(matches_filename, sep=None, index_col=0)
+    scores = read_csv(scores_filename, sep=None, index_col=0)
+
+    scores_long = create_long_table(scores, 'score')
+    matches_long = create_long_table(matches, 'matches')
+
+    combined = join_df(matches_long, scores_long, on=['compound'], how='inner')
+    return combined
+
+
+@click.group()
+@click.option('--sf', 'scores_filename', type=click.Path(exists=True), required=True)
+@click.option('--mf', 'matches_filename', type=click.Path(exists=True), required=True)
+@click.option('--o', 'output_filename', type=click.Path(writable=True), required=True)
+@click.pass_context
+def cli(ctx, scores_filename, matches_filename, output_filename):
+    ctx.ensure_object(dict)
+    ctx.obj['data'] = load_data(scores_filename, matches_filename)
+    pass
+
+
+@cli.command()
+@click.option('--st', 'scores_threshold', type=float, required=True)
+@click.option('--mt', 'matches_threshold', type=float, required=True)
+@click.pass_context
+def get_thresholded_data(ctx, scores_threshold, matches_threshold):
+    result = filter_thresholds(ctx.obj['data'], scores_threshold, matches_threshold)
+    return result
+
+
+@cli.command()
+@click.option('--k', 'k', type=int, required=True)
+@click.pass_context
+def get_top_k_data(ctx, k):
+    result = get_top_k_matches(ctx.obj['data'], k)
+    return result
+
+
+@cli.resultcallback()
+def write_output(result: DataFrame, scores_filename, matches_filename, output_filename):
+    input_file = read_csv(scores_filename, sep=None, iterator=True)
+    sep = input_file._engine.data.dialect.delimiter
+
+    result = result.reset_index().rename(columns={'level_0': 'query', 'compound': 'reference'})
+    result.to_csv(output_filename, sep=sep, index=False)
+
+
+if __name__ == '__main__':
+    cli(obj={})